Key terms - Individual - Any possible solution
- Population - Group of all individuals
- Search Space - All possible solutions to the problem
- Chromosome - Blueprint for an individual
- Trait - Possible aspect (features) of an individual
- Allele - Possible settings of trait (black, blond, etc.)
- Locus - The position of a gene on the chromosome
- Genome - Collection of all chromosomes for an individual
- Genotype:
- – Particular set of genes in a genome
- Phenotype:
- – Physical characteristic of the genotype (smart, beautiful, healthy, etc.)
Genotype and Phenotype GA Requirements - A typical genetic algorithm requires two things to be defined:
- a genetic representation of the solution domain, and
- a fitness function to evaluate the solution domain.
- A standard representation of the solution is as an array of bits. Arrays of other types and structures can be used in essentially the same way.
- The main property that makes these genetic representations convenient is that their parts are easily aligned due to their fixed size, that facilitates simple crossover operation.
- Variable length representations may also be used, but crossover implementation is more complex in this case.
- Tree-like representations are explored in Genetic programming.
Representation - Chromosomes could be:
- Bit strings (0101 ... 1100)
- Real numbers (43.2 -33.1 ... 0.0 89.2)
- Permutations of element (E11 E3 E7 ... E1 E15)
- Lists of rules (R1 R2 R3 ... R22 R23)
- Program elements (genetic programming)
- ... any data structure ...
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